In business, there is an old saying long-heeded by many successful executives: “no risk, no reward.” Though somewhat cliché, this aphorism identifies risk as an important factor in making business decisions. Every business decision entails some degree of risk and, in most situations, this risk is typically evaluated subjectively based on knowledge, experience, perception, and intuition. For example, a small business is confronted with risk whenever the business hires, fires, buys, sells, or even opens its doors to the public. In many, if not most, situations, these risks are managed entirely by subjective analysis—managers rely on intuition refined by years of experience instead of performing systematic objective analysis.
So long as intuition and subjective analysis work, systematic objective techniques may reduce efficiency, requiring unnecessary calculations, computation, and documentation to arrive at or near the same result. However, complex business decisions may be beyond the intuitive abilities of most managers. For example, a large international corporation engaged in a multitude of transactions may be subject to substantial risk of currency fluctuation. Consider an agreement to supply widgets to a customer at a price specified in one currency. If widget components are obtained at agreed upon prices in another currency and the widgets are assembled by employees paid in a third currency, then currency fluctuations could render the widget supply business unprofitable. However, once the risks of currency fluctuation are defined, steps may be taken to mitigate the risk. As complexity increases, business managers may use computational decision analysis systems for assistance.
A variety of commercial decision analysis systems are available for purchase including, for example, the following software applications: software applications including @Risk, Crystal Ball®, and XLSim®. The most common automated decision support approach is Monte Carlo simulation. Using Monte Carlo simulation, a model is created by identifying uncertain variables, assigning probability distributions to each uncertain variable, and by defining the relationship between output variables and the uncertain variables. Then, random values are generated based on the probability distributions. The resulting values then may be analyzed to determine likely outcomes.
The efficacy of a decision analysis system using simulation depends on the quality and accuracy of the model. A variety of design flaws can affect the predictive ability of the system, such as, for example, uncertain variables may depend on one another, skewing simulation results; relevant variables may be omitted; and distributions may be inaccurate. For maximum effectiveness, probability distributions need to be carefully crafted to ensure maximum predictive value and to ensure dependencies are fully represented. The skills of an experienced statistician would certainly be beneficial. Additionally, business managers may have an interest in swaying predicted outcomes for their own personal gain. It is desirable to provide decision analysis tools and techniques to alleviate these concerns.